OpenRaiser/Intern-Atlas huggingface dataset:
SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations
Venue: arXiv cs.LG 2025
Year: 2025

We address a fundamental challenge in Reinforcement Learning from Interaction Demonstration (RLID): demonstration noise and coverage limitations. While existing data collection approaches provide valuable interaction demonstrations, they often yield sparse, disconnected, and noisy trajectories that fail to capture the full spectrum of possible skill variations and transitions. Our key insight is that despite noisy and sparse demonstrations, there exist infinite physically feasible trajectories that naturally bridge between demonstrated skills or emerge from their neighboring states, forming a continuous space of possible skill variations and transitions. Building upon this insight, we present two data augmentation techniques: a Stitched Trajectory Graph (STG) that discovers potential transitions between demonstration skills, and a State Transition Field (STF) that establishes unique connections for arbitrary states within the demonstration neighborhood. To enable effective RLID with augmented data, we develop an Adaptive Trajectory Sampling (ATS) strategy for dynamic curriculum generation and a historical encoding mechanism for memory-dependent skill learning. Our approach enables robust skill acquisition that significantly generalizes beyond the reference demonstrations. Extensive experiments across diverse interaction tasks demonstrate substantial improvements over state-of-the-art methods in terms of convergence stability, generalization capability, and recovery robustness.
SignSplat: Rendering Sign Language via Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

State-of-the-art approaches for conditional human body rendering via Gaussian splatting typically focus on simple body motions captured from many views. This is often in the context of dancing or walking. However, for more complex use cases, such as sign language, we care less about large body motion and more about subtle and complex motions of the hands and face. The problems of building high fidelity models are compounded by the complexity of capturing multi-view data of sign. The solution is to make better use of sequence data, ensuring that we can overcome the limited information from only a few views by exploiting temporal variability. Nevertheless, learning from sequence-level data requires extremely accurate and consistent model fitting to ensure that appearance is consistent across complex motions. We focus on how to achieve this, constraining mesh parameters to build an accurate Gaussian splatting framework from few views capable of modelling subtle human motion. We leverage regularization techniques on the Gaussian parameters to mitigate overfitting and rendering artifacts. Additionally, we propose a new adaptive control method to densify Gaussians and prune splat points on the mesh surface. To demonstrate the accuracy of our approach, we render novel sequences of sign language video, building on neural machine translation approaches to sign stitching. On benchmark datasets, our approach achieves state-of-the-art performance; and on highly articulated and complex sign language motion, we significantly outperform competing approaches.
Large Language Models are overconfident and amplify human bias
Venue: arXiv cs.SE 2025
Year: 2025

Large language models (LLMs) are revolutionizing every aspect of society. They are increasingly used in problem-solving tasks to substitute human assessment and reasoning. LLMs are trained on what humans write and are thus exposed to human bias. We evaluate whether LLMs inherit one of the most widespread human biases: overconfidence. We algorithmically construct reasoning problems with known ground truths. We prompt LLMs to answer these problems and assess the confidence in their answers, closely following similar protocols in human experiments. We find that all five LLMs we study are overconfident: they overestimate the probability that their answer is correct between 20% and 60%. Humans have accuracy similar to the more advanced LLMs, but far lower overconfidence. Although humans and LLMs are similarly biased in questions which they are certain they answered correctly, a key difference emerges between them: LLM bias increases sharply relative to humans if they become less sure that their answers are correct. We also show that LLM input has ambiguous effects on human decision making: LLM input leads to an increase in the accuracy, but it more than doubles the extent of overconfidence in the answers.
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Video super-resolution (VSR) can achieve better performance compared to single image super-resolution by additionally leveraging temporal information. In particular, the recurrent-based VSR model exploits long-range temporal information during inference and achieves superior detail restoration. However, effectively learning these long-term dependencies within long videos remains a key challenge. To address this, we propose LRTI-VSR, a novel training framework for recurrent VSR that efficiently leverages Long-Range Refocused Temporal Information. Our framework includes a generic training strategy that utilizes temporal propagation features from long video clips while training on shorter video clips. Additionally, we introduce a refocused intra&inter-frame transformer block which allows the VSR model to selectively prioritize useful temporal information through its attention module while further improving inter-frame information utilization in the FFN module. We evaluate LRTI-VSR on both CNN and transformer-based VSR architectures, conducting extensive ablation studies to validate the contribution of each component. Experiments on long-video test sets demonstrate that LRTI-VSR achieves state-of-the-art performance while maintaining training and computational efficiency.
ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
Venue: arXiv cs.CV 2025
Year: 2025

Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling this via two synergistic components. The Prototype Interaction Layer (PIL) provides controlled normality modeling using a small set of learnable prototypes, establishing a robust baseline without being overwhelmed by dominant normal data. The Pseudo-Instance Discriminative Enhancement (PIDE) loss boosts separability by applying targeted contrastive learning exclusively to the most reliable extreme-scoring instances (highest/lowest scores). ProDisc-VAD achieves strong AUCs (97.98% ShanghaiTech, 87.12% UCF-Crime) using only 0.4M parameters, over 800x fewer than recent ViT-based methods like VadCLIP. Code is available at https://github.com/modadundun/ProDisc-VAD.
Scalable Genomic Context Analysis with GCsnap2 on HPC Clusters
Venue: arXiv cs.DC 2025
Year: 2025

GCsnap2 Cluster is a scalable, high performance tool for genomic context analysis, developed to overcome the limitations of its predecessor, GCsnap1 Desktop. Leveraging distributed computing with mpi4py[.]futures, GCsnap2 Cluster achieved a 22x improvement in execution time and can now perform genomic context analysis for hundreds of thousands of input sequences in HPC clusters. Its modular architecture enables the creation of task-specific workflows and flexible deployment in various computational environments, making it well suited for bioinformatics studies of large-scale datasets. This work highlights the potential for applying similar approaches to solve scalability challenges in other scientific domains that rely on large-scale data analysis pipelines.
Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
Venue: arXiv cs.CR 2025
Year: 2025

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.
The Voynich Codex Decoded: Statistical Symbolism and Scroll-Wide Logic
Venue: arXiv cs.SC 2025
Year: 2025

This paper introduces a structured decoding framework for the Voynich Manuscript, based on mathematical rhythm, symbolic transformation, and glyph-level recursion. Rather than interpret symbols phonetically, this method decodes them by structural roles and spatial pacing. Using scroll-wide sequencing, the system tracks prime number grouping, Fibonacci clustering, and golden ratio alignment. These symbolic structures are validated using a ten-part chi-squared test suite and Boolean logic. The method is falsifiable and reproducible. Scroll sections like f57v, f88v, and f91r are used to demonstrate glyph flow, breath-segment patterns, and tri-dot alignment. This decoding strategy challenges assumptions about pre-phonetic manuscripts and proposes a new lens for interpreting symbolic logic.
Parameter-Efficient Transformer Embeddings
Venue: arXiv cs.CL 2025
Year: 2025

Embedding layers in transformer-based NLP models typically account for the largest share of model parameters, scaling with vocabulary size but not yielding performance gains proportional to scale. We propose an alternative approach in which token embedding vectors are first generated deterministically, directly from the token IDs using a Fourier expansion of their normalized values, followed by a lightweight multilayer perceptron (MLP) that captures higher-order interactions. We train standard transformers and our architecture on natural language inference tasks (SNLI and MNLI), and evaluate zero-shot performance on sentence textual similarity (STS-B). Our results demonstrate that the proposed method achieves competitive performance using significantly fewer parameters, trains faster, and operates effectively without the need for dropout. This proof-of-concept study highlights the potential for scalable, memory-efficient language models and motivates further large-scale experimentation based on our findings.
A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm
Venue: arXiv cs.SE 2025
Year: 2025

The emergence of Large Code Models (LCMs) has transformed software engineering (SE) automation, driving significant advancements in tasks such as code generation, source code documentation, code review, and bug fixing. However, these advancements come with trade-offs: achieving high performance often entails exponential computational costs, reduced interpretability, and an increasing dependence on data-intensive models with hundreds of billions of parameters. In this paper, we propose Neurosymbolic Software Engineering, in short NSE, as a promising paradigm combining neural learning with symbolic (rule-based) reasoning, while strategically introducing a controlled source of chaos to simulate the complex dynamics of real-world software systems. This hybrid methodology aims to enhance efficiency, reliability, and transparency in AI-driven software engineering while introducing controlled randomness to adapt to evolving requirements, unpredictable system behaviors, and non-deterministic execution environments. By redefining the core principles of AI-driven software engineering automation, NSE lays the groundwork for solutions that are more adaptable, transparent, and closely aligned with the evolving demands of modern software development practices.
Content Addressable Memory Design with Reference Resistor for Improved Search Resolution
Venue: arXiv cs.ET 2025
Year: 2025

Despite the parallel in-memory search capabilities of content addressable memories (CAMs), their use in applications is constrained by their limited resolution that worsens as they are scaled to larger arrays or advanced nodes. In this work we present experimental results for a novel back-end-of-line compatible reference resistive device that can significantly improve the search resolution of CAMs implemented with CMOS and beyond-CMOS technologies to less than or equal to 5-bits.
What Is AI Safety? What Do We Want It to Be?
Venue: arXiv cs.CY 2025
Year: 2025

The field of AI safety seeks to prevent or reduce the harms caused by AI systems. A simple and appealing account of what is distinctive of AI safety as a field holds that this feature is constitutive: a research project falls within the purview of AI safety just in case it aims to prevent or reduce the harms caused by AI systems. Call this appealingly simple account The Safety Conception of AI safety. Despite its simplicity and appeal, we argue that The Safety Conception is in tension with at least two trends in the ways AI safety researchers and organizations think and talk about AI safety: first, a tendency to characterize the goal of AI safety research in terms of catastrophic risks from future systems; second, the increasingly popular idea that AI safety can be thought of as a branch of safety engineering. Adopting the methodology of conceptual engineering, we argue that these trends are unfortunate: when we consider what concept of AI safety it would be best to have, there are compelling reasons to think that The Safety Conception is the answer. Descriptively, The Safety Conception allows us to see how work on topics that have historically been treated as central to the field of AI safety is continuous with work on topics that have historically been treated as more marginal, like bias, misinformation, and privacy. Normatively, taking The Safety Conception seriously means approaching all efforts to prevent or mitigate harms from AI systems based on their merits rather than drawing arbitrary distinctions between them.
Guarding Terrains with Guards on a Line
Venue: arXiv cs.CG 2025
Year: 2025

Given an $x$-monotone polygonal chain $T$ with $n$ vertices, and an integer $k$, we consider the problem of finding the lowest horizontal line $L$ lying above $T$ with $k$ point guards lying on $L$, so that every point on the chain is \emph{visible} from some guard. A natural optimization is to minimize the $y$-coordinate of $L$. We present an algorithm for finding the optimal placements of $L$ and $k$ point guards for $T$ in $O(k^2λ_{k-1}(n)\log n)$ time for even numbers $k\ge 2$, and in $O(k^2λ_{k-2}(n)\log n)$ time for odd numbers $k \ge 3$, where $λ_{s}(n)$ is the length of the longest $(n,s)$-Davenport-Schinzel sequence. We also study a variant with an additional requirement that $T$ is partitioned into $k$ subchains, each subchain is paired with exactly one guard, and every point on a subchain is visible from its paired guard. When $L$ is fixed, we can place the minimum number of guards in $O(n)$ time. When the number $k$ of guards is fixed, we can find an optimal placement of $L$ with $k$ point guards lying on $L$ in $O(kn)$ time.
Encrypted Federated Search Using Homomorphic Encryption
Venue: arXiv cs.CR 2025
Year: 2025

The sharing of information between agencies is effective in dealing with cross-jurisdictional criminal activities; however, such sharing is often restricted due to concerns about data privacy, ownership, and compliance. Towards this end, this work has introduced a privacy-preserving federated search system that allows law enforcement agencies to conduct queries on encrypted criminal databases by utilizing Homomorphic Encryption (HE). The key innovation here is the ability to execute encrypted queries across distributed databases, without the decryption of the data, thus preserving end-to-end confidentiality. In essence, this approach meets stringent privacy requirements in the interests of national security and regulatory compliance. The system incorporates the CKKS and BFV scheme embedded within TenSEAL, with each agency holding its key pair in a centralized key management table. In this federated search, encrypted queries are computed on the server side, and only authorized clients can decrypt the computed results. The matching of agencies is flexible for working in real-time while at the same time being secure and scalable while preserving control over data and the integrity of the process. Experimental results demonstrate the model. This paper also provide the implementation code and other details.
Bielik 11B v2 Technical Report
Venue: arXiv cs.CL 2025
Year: 2025

We present Bielik 11B v2, a state-of-the-art language model optimized for Polish text processing. Built on the Mistral 7B v0.2 architecture and scaled to 11B parameters using depth up-scaling, this model demonstrates exceptional performance across Polish language benchmarks while maintaining strong cross-lingual capabilities. We introduce two key technical innovations: Weighted Instruction Cross-Entropy Loss, which optimizes learning across diverse instruction types by assigning quality-based weights to training examples, and Adaptive Learning Rate, which dynamically adjusts based on context length. Comprehensive evaluation across multiple benchmarks demonstrates that Bielik 11B v2 outperforms many larger models, including those with 2-6 times more parameters, and significantly surpasses other specialized Polish language models on tasks ranging from linguistic understanding to complex reasoning. The model's parameter efficiency and extensive quantization options enable deployment across various hardware configurations, advancing Polish language AI capabilities and establishing new benchmarks for resource-efficient language modeling in less-represented languages.
ZeloS -- A Research Platform for Early-Stage Validation of Research Findings Related to Automated Driving
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents ZeloS, a research platform designed and built for practical validation of automated driving methods in an early stage of research. We overview ZeloS' hardware setup and automation architecture and focus on motion planning and control. ZeloS weighs 69 kg, measures a length of 117 cm, and is equipped with all-wheel steering, all-wheel drive, and various onboard sensors for localization. The hardware setup and the automation architecture of ZeloS are designed and built with a focus on modularity and the goal of being simple yet effective. The modular design allows the modification of individual automation modules without the need for extensive onboarding into the automation architecture. As such, this design supports ZeloS in being a versatile research platform for validating various automated driving methods. The motion planning component and control of ZeloS feature optimization-based methods that allow for explicitly considering constraints. We demonstrate the hardware and automation setup by presenting experimental data.
El Agente: An Autonomous Agent for Quantum Chemistry
Venue: arXiv cs.AI 2025
Year: 2025

Computational chemistry tools are widely used to study the behaviour of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.
Automating Automotive Software Development: A Synergy of Generative AI and Model-Based Methods
Venue: arXiv cs.SE 2025
Year: 2025

As the automotive industry shifts its focus toward software-defined vehicles, the need for faster and reliable software development continues to grow. However, traditional methods show their limitations. The rise of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), introduces new opportunities to automate automotive software development tasks such as requirement analysis and code generation. However, due to the complexity of automotive systems, where software components must interact with each other seamlessly, challenges remain in software integration and system-level validation. In this paper, we propose to combine GenAI with model-driven engineering to automate automotive software development. Our approach uses LLMs to convert free-text requirements into event chain descriptions and to generate platform-independent software components that realize the required functionality. At the same time, formal models are created based on event chain descriptions to support system validation and the generation of integration code for integrating generated software components in the whole vehicle system through middleware. This approach increases development automation while enabling formal analysis to improve system reliability. As a proof of concept, we used GPT-4o to implement our method and tested it in the CARLA simulation environment with ROS2 middleware. We evaluated the system in a simple Autonomous Emergency Braking scenario.
Trustworthy Inter-Provider Agreements in 6G Using a Privacy-Enabled Hybrid Blockchain Framework
Venue: arXiv cs.NI 2025
Year: 2025

Inter-provider agreements are central to 6G networks, where administrative domains must securely and dynamically share services. To address the dual need for transparency and confidentiality, we propose a privacy-enabled hybrid blockchain setup using Hyperledger Besu, integrating both public and private transaction workflows. The system enables decentralized service registration, selection, and SLA breach reporting through role-based smart contracts and privacy groups. We design and deploy a proof-of-concept implementation, evaluating performance using end-to-end latency as a key metric within privacy groups. Results show that public interactions maintain stable latency, while private transactions incur additional overhead due to off-chain coordination. The block production rate governed by IBFT 2.0 had limited impact on private transaction latency, due to encryption and peer synchronization. Lessons learned highlight design considerations for smart contract structure, validator management, and scalability patterns suitable for dynamic inter-domain collaboration. Our findings offer practical insights for deploying trustworthy agreement systems in 6G networks using privacy-enabled hybrid blockchains.